Click here for interactive plots:

https://kforthman.shinyapps.io/COVID19_Interactive_Plots/

Click here to see how cities and counties overlap:

https://kforthman.shinyapps.io/500citiescounties

Compare COVID stats to Neighborhood Factors

data.cor <- cor(county.Demo_and_Covid.allcounties[,-1], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

Compare COVID stats to 500 cities data and Neighborhood Factors

data.cor2 <- cor(county.Demo_and_Covid.500counties[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[7:13,c(1:5, 14:42,6)], upper = 'ellipse', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1274.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2919 -0.3550 -0.0398  0.2656  5.7979 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000006157 0.002481
##  Residual             0.000015378 0.003921
## Number of obs: 192, groups:  stateID, 35
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0113318282   0.0116504459  98.3669480205
## Affluence                    0.0047923912   0.0011791283 144.8966850724
## Singletons.in.Tract          0.0009250241   0.0009995901 171.9703083064
## Seniors.in.Tract             0.0005272872   0.0012995747 171.8227636229
## African.Americans.in.Tract   0.0012353859   0.0011067942 171.9140817414
## Noncitizens.in.Tract         0.0016276525   0.0008513710 152.3349385176
## High.BP                      0.0000158635   0.0002110896 156.3473087866
## Binge.Drinking               0.0003605972   0.0002021544  73.7103832564
## Cancer                      -0.0019924817   0.0012640503 147.0794138021
## Asthma                       0.0001263159   0.0006814179  77.5949788382
## Heart.Disease                0.0029143503   0.0015807173 123.5877712327
## COPD                        -0.0002263934   0.0013009092 120.9490588077
## Smoking                     -0.0002042044   0.0002623405 138.3662234865
## Diabetes                    -0.0008197894   0.0006410012 126.0697103519
## No.Physical.Activity         0.0000503457   0.0002424726 136.0306027652
## Obesity                      0.0003645168   0.0002016578 163.3544434967
## Poor.Sleeping.Habits         0.0000903992   0.0001827405 159.6688883982
## Poor.Mental.Health          -0.0000522610   0.0005535499  50.8335905645
## Testing_Rate                 0.0000007590   0.0000002951  45.6405760360
## Hospitalization_Rate        -0.0001247906   0.0001211159  32.1085677074
##                            t value  Pr(>|t|)    
## (Intercept)                 -0.973    0.3331    
## Affluence                    4.064 0.0000787 ***
## Singletons.in.Tract          0.925    0.3561    
## Seniors.in.Tract             0.406    0.6854    
## African.Americans.in.Tract   1.116    0.2659    
## Noncitizens.in.Tract         1.912    0.0578 .  
## High.BP                      0.075    0.9402    
## Binge.Drinking               1.784    0.0786 .  
## Cancer                      -1.576    0.1171    
## Asthma                       0.185    0.8534    
## Heart.Disease                1.844    0.0676 .  
## COPD                        -0.174    0.8621    
## Smoking                     -0.778    0.4377    
## Diabetes                    -1.279    0.2033    
## No.Physical.Activity         0.208    0.8358    
## Obesity                      1.808    0.0725 .  
## Poor.Sleeping.Habits         0.495    0.6215    
## Poor.Mental.Health          -0.094    0.9252    
## Testing_Rate                 2.572    0.0134 *  
## Hospitalization_Rate        -1.030    0.3105    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.011                                                        
## Sngltns.n.T  0.022  0.068                                                 
## Snrs.n.Trct  0.472  0.344  0.189                                          
## Afrcn.Am..T  0.123  0.147 -0.386  0.150                                   
## Nnctzns.n.T -0.002  0.119  0.035  0.086 -0.128                            
## High.BP     -0.079  0.261  0.018  0.072 -0.066  0.342                     
## Bing.Drnkng -0.393 -0.122 -0.277 -0.117  0.064 -0.018  0.130              
## Cancer      -0.553 -0.104  0.211 -0.250 -0.077 -0.083 -0.334 -0.051       
## Asthma      -0.412 -0.100 -0.265 -0.213  0.075  0.100  0.115  0.038  0.042
## Heart.Dises -0.183  0.062 -0.310 -0.174  0.249 -0.131  0.059  0.067 -0.488
## COPD         0.574  0.005  0.161  0.264 -0.044  0.240  0.067  0.026 -0.256
## Smoking     -0.096  0.117 -0.177 -0.120 -0.044  0.069 -0.033 -0.278  0.082
## Diabetes     0.152 -0.382 -0.089 -0.192 -0.301 -0.235 -0.554  0.039  0.237
## N.Physcl.Ac -0.211  0.069  0.110  0.015 -0.019 -0.218 -0.008  0.118  0.442
## Obesity     -0.025  0.379  0.478  0.283  0.104  0.162 -0.100 -0.187  0.116
## Pr.Slpng.Hb -0.407 -0.393  0.111 -0.325 -0.278 -0.070 -0.185  0.109  0.094
## Pr.Mntl.Hlt -0.365  0.224 -0.053 -0.027  0.071 -0.120  0.026  0.122  0.350
## Testing_Rat  0.221 -0.132  0.027  0.019  0.016 -0.027 -0.038 -0.075 -0.171
## Hsptlztn_Rt -0.116 -0.130 -0.052 -0.165 -0.053 -0.075 -0.037 -0.079 -0.071
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.326                                                        
## COPD        -0.408 -0.581                                                 
## Smoking      0.105  0.173 -0.464                                          
## Diabetes    -0.141 -0.360  0.009  0.212                                   
## N.Physcl.Ac  0.066 -0.347 -0.011 -0.289 -0.163                            
## Obesity     -0.211 -0.088  0.150 -0.253 -0.369 -0.003                     
## Pr.Slpng.Hb  0.089  0.257 -0.159 -0.080 -0.034 -0.157 -0.138              
## Pr.Mntl.Hlt -0.254  0.076 -0.454  0.023 -0.009  0.002  0.022 -0.127       
## Testing_Rat -0.298 -0.086  0.238  0.099  0.138 -0.288  0.087 -0.126 -0.145
## Hsptlztn_Rt  0.045  0.152 -0.104  0.060 -0.024 -0.005  0.013  0.007 -0.093
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.080
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)", data = county.Demo_and_Covid.500counties)

print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -2427.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8334 -0.3825 -0.0836  0.2882  6.5084 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000007872 0.002806
##  Residual             0.000013290 0.003646
## Number of obs: 326, groups:  stateID, 51
## 
## Fixed effects:
##                                Estimate   Std. Error           df t value
## (Intercept)                 -0.02422838   0.00816912 192.13945306  -2.966
## Affluence                    0.00306783   0.00074268 302.01017953   4.131
## Singletons.in.Tract          0.00073208   0.00069388 301.27617807   1.055
## Seniors.in.Tract             0.00023558   0.00087633 304.64315604   0.269
## African.Americans.in.Tract   0.00193767   0.00084699 306.90000915   2.288
## Noncitizens.in.Tract         0.00189600   0.00068268 270.97498421   2.777
## High.BP                     -0.00003196   0.00015324 298.31315117  -0.209
## Binge.Drinking               0.00040621   0.00016076 158.14883530   2.527
## Cancer                      -0.00028148   0.00089839 265.63184646  -0.313
## Asthma                       0.00086186   0.00053271 140.79048500   1.618
## Heart.Disease                0.00320794   0.00115189 209.68523086   2.785
## COPD                        -0.00137576   0.00087194 204.29695038  -1.578
## Smoking                     -0.00019795   0.00020166 249.76729692  -0.982
## Diabetes                    -0.00114700   0.00043226 268.25920226  -2.653
## No.Physical.Activity         0.00031914   0.00017356 236.62271103   1.839
## Obesity                      0.00025681   0.00014071 307.98586543   1.825
## Poor.Sleeping.Habits         0.00023748   0.00013543 296.98036928   1.754
## Poor.Mental.Health          -0.00014904   0.00045166 103.08747962  -0.330
##                             Pr(>|t|)    
## (Intercept)                  0.00340 ** 
## Affluence                  0.0000469 ***
## Singletons.in.Tract          0.29225    
## Seniors.in.Tract             0.78825    
## African.Americans.in.Tract   0.02284 *  
## Noncitizens.in.Tract         0.00586 ** 
## High.BP                      0.83493    
## Binge.Drinking               0.01249 *  
## Cancer                       0.75429    
## Asthma                       0.10793    
## Heart.Disease                0.00584 ** 
## COPD                         0.11615    
## Smoking                      0.32725    
## Diabetes                     0.00844 ** 
## No.Physical.Activity         0.06720 .  
## Obesity                      0.06896 .  
## Poor.Sleeping.Habits         0.08054 .  
## Poor.Mental.Health           0.74209    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence   -0.049                                                        
## Sngltns.n.T -0.056  0.043                                                 
## Snrs.n.Trct  0.396  0.293  0.073                                          
## Afrcn.Am..T  0.242  0.076 -0.405  0.202                                   
## Nnctzns.n.T -0.072  0.153  0.125  0.057 -0.190                            
## High.BP     -0.095  0.157  0.099  0.007 -0.234  0.328                     
## Bing.Drnkng -0.487 -0.041 -0.205 -0.069  0.042 -0.076  0.149              
## Cancer      -0.495 -0.095  0.231 -0.173 -0.073 -0.067 -0.329 -0.020       
## Asthma      -0.268 -0.097 -0.262 -0.121 -0.013  0.211  0.053  0.007 -0.158
## Heart.Dises -0.058  0.076 -0.301 -0.132  0.213 -0.054 -0.001  0.034 -0.602
## COPD         0.479  0.011  0.128  0.172 -0.005  0.156  0.059  0.060 -0.213
## Smoking     -0.044  0.105 -0.119 -0.137 -0.105  0.159 -0.083 -0.327  0.158
## Diabetes     0.036 -0.301 -0.079 -0.133 -0.230 -0.254 -0.445  0.075  0.366
## N.Physcl.Ac -0.116  0.034  0.101  0.079  0.059 -0.274  0.004  0.125  0.337
## Obesity     -0.065  0.383  0.398  0.202  0.133  0.194 -0.103 -0.148  0.119
## Pr.Slpng.Hb -0.385 -0.351  0.162 -0.326 -0.322 -0.046 -0.156  0.087  0.028
## Pr.Mntl.Hlt -0.354  0.183 -0.007  0.021  0.051 -0.165  0.027  0.131  0.417
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H
## Affluence                                                          
## Sngltns.n.T                                                        
## Snrs.n.Trct                                                        
## Afrcn.Am..T                                                        
## Nnctzns.n.T                                                        
## High.BP                                                            
## Bing.Drnkng                                                        
## Cancer                                                             
## Asthma                                                             
## Heart.Dises  0.336                                                 
## COPD        -0.323 -0.490                                          
## Smoking      0.144  0.082 -0.476                                   
## Diabetes    -0.106 -0.431 -0.009  0.278                            
## N.Physcl.Ac -0.023 -0.361  0.087 -0.274 -0.169                     
## Obesity     -0.127 -0.021  0.091 -0.220 -0.376 -0.045              
## Pr.Slpng.Hb  0.000  0.240 -0.093 -0.167 -0.060 -0.154 -0.115       
## Pr.Mntl.Hlt -0.437 -0.066 -0.389 -0.028  0.072 -0.084  0.026 -0.082

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Pink highlights the last 14 days.

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

twoweek.col <- c(rep("grey", n.days-day.first.case-13), rep("pink", 14))

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$rise.cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Cases of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$rise.deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Deaths of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)